Comparison of land-cover classification methods in the Brazilian Amazon Basin.

Four distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-LSMA). Each of the classifiers used both Landsat Thematic Mapper data and identical field-based training sample datasets in a western Brazilian Amazon study area. Seven land-cover classes? mature forest, advanced secondary succession, initial secondary succession, pasture lands, agricultural lands, bare lands, and water?were classified. Classification results indicate that the DTC-LSMA and ECHO classifiers were more accurate than were the MDC and MLC. The overall accuracy of the DTCLSMA approach was 86 percent with a 0.82 kappa coefficient and ECHO had an accuracy of 83 percent with a 0.79 kappa coefficient. The accuracy of the other classifiers ranged from 77 to 80 percent with kappa coefficients from 0.72 to 0.75.

Bibliographic Details

Main Authors:	LU, D., MAUSEL, P., BATISTELLA, M., MORAN, E.
Other Authors:	1-2 e 4: Indiana University; 3: Embrapa Monitoramento por Satélite.
Format:	Artigo de periódico biblioteca
Language:	English eng
Published:	2004-04-29
Subjects:	Mapeamento, Amazonia brasileira, Amazonas., Bacia Hidrográfica, Floresta Tropical Úmida, Satélite.,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/17039
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